©2014 Knowledgent Group Inc. All Rights Reserved
Pragmatic Enterprise Architecture
In the Era of Big Data and Quantum Comp...
©2014 Knowledgent Group Inc. All Rights Reserved
Agenda
• Bio / Who is Jim Luisi?
• WIIFM / What’s in it for me?
• Enterpr...
©2014 Knowledgent Group Inc. All Rights Reserved
Bio
• 30 years
• Business
- Business owner
- Big Data in advertising indu...
©2014 Knowledgent Group Inc. All Rights Reserved
• 99.9% of the Big Data mystery will be removed
• You’ll be able to engag...
©2014 Knowledgent Group Inc. All Rights Reserved
• EA was a corporate committee of generalists
• EA focused on a few parts...
©2014 Knowledgent Group Inc. All Rights Reserved
• EA will render a holistic view of automation
• Standards and governance...
©2014 Knowledgent Group Inc. All Rights Reserved
• Subject matter experts will make the technology ‘D’
• SMEs will collabo...
©2014 Knowledgent Group Inc. All Rights Reserved
• From a storage perspective
- Lots of storage
• From a distribution pers...
©2014 Knowledgent Group Inc. All Rights Reserved
Why Has Big Data become more compelling?
• Big Data has become
- More adv...
©2014 Knowledgent Group Inc. All Rights Reserved
What architectural disciplines relate to Big Data?
Business
Architecture
...
©2014 Knowledgent Group Inc. All Rights Reserved
• Variety of Big Data
- database technologies
- hardware technologies
- a...
©2014 Knowledgent Group Inc. All Rights Reserved
• All requiring a solid foundation
• None of which are free
• Adding to t...
©2014 Knowledgent Group Inc. All Rights Reserved
• Competing open source foundations
- Apache Software Foundation (ASF)
- ...
©2014 Knowledgent Group Inc. All Rights Reserved
TPM / Competing Big Data Frameworks
• and more…
©2014 Knowledgent Group Inc. All Rights Reserved
• Applications use assorted Big Data technologies
- Open source
- License...
©2014 Knowledgent Group Inc. All Rights Reserved
Parent Child Data
A
B C
D E X
Hierarchical
Parent First Set A Last Set A ...
©2014 Knowledgent Group Inc. All Rights Reserved
There are database architectures of
TPM / Traditional Big Data Technologi...
©2014 Knowledgent Group Inc. All Rights Reserved
Microsoft HDInsight Intel Distribution (IDH)
And there are database archi...
©2014 Knowledgent Group Inc. All Rights Reserved
TPM / Newer SQL Types
NoSQL is a new way of
thinking about databases,
fou...
©2014 Knowledgent Group Inc. All Rights Reserved
What does ACID mean?
• Atomicity
- refers to all or nothing for a logical...
©2014 Knowledgent Group Inc. All Rights Reserved
TPM / Proprietary Big Data Product Names
• US Government
- Hypernumbers
•...
©2014 Knowledgent Group Inc. All Rights Reserved
TPM / Quantum Computing
• Probabilistic computing
- confidence levels
- t...
©2014 Knowledgent Group Inc. All Rights Reserved
What are some Big Data Use Case Families?
• Document / content management...
©2014 Knowledgent Group Inc. All Rights Reserved
Document / Content Management Use Cases
IT Documents
 presentations
 wo...
©2014 Knowledgent Group Inc. All Rights Reserved
Online Transaction Processing Use Cases
Candidate Solutions
E-Commerce
 ...
©2014 Knowledgent Group Inc. All Rights Reserved
Data Warehousing Use Cases
Candidate
Solutions
Financial Industry
 Under...
©2014 Knowledgent Group Inc. All Rights Reserved
Real Time Analytics Use Cases
Candidate Solutions
 Investment risk
 Ope...
©2014 Knowledgent Group Inc. All Rights Reserved
Batch Analytics Use Cases
Candidate Solutions
Candidate Requirement Types...
©2014 Knowledgent Group Inc. All Rights Reserved
Geographic Information System Use Cases
Candidate Solutions
Candidate Req...
©2014 Knowledgent Group Inc. All Rights Reserved
Search Use Cases
Candidate SolutionsCandidate Requirement Types
Usability...
©2014 Knowledgent Group Inc. All Rights Reserved
Predictive Analytics Use Cases
Candidate Solutions
Financial Industry
 C...
©2014 Knowledgent Group Inc. All Rights Reserved
Particular Deterministic Algorithmic Use Cases
Candidate Solutions
Any In...
©2014 Knowledgent Group Inc. All Rights Reserved
NP Deterministic Algorithmic Use Cases
Candidate Solutions
(Proprietary)
...
©2014 Knowledgent Group Inc. All Rights Reserved
What is NP?
• NP stands for ‘non-deterministic polynomial time’
• Convent...
©2014 Knowledgent Group Inc. All Rights Reserved
Probabilistic Use Cases
• Cryptography (aka code breaking)
• Prime number...
©2014 Knowledgent Group Inc. All Rights Reserved
Hardware architecture / AQC
• Commercially available platforms
- 128 qubi...
©2014 Knowledgent Group Inc. All Rights Reserved
Software Architecture / AQC
full disjunctive normal form
©2014 Knowledgent Group Inc. All Rights Reserved
AQC Software - Bottom Up
• Programming the hardware
- is all about managi...
©2014 Knowledgent Group Inc. All Rights Reserved
AQC Software - Bottom Up (cont’d)
• System Application Program Interface
...
©2014 Knowledgent Group Inc. All Rights Reserved
AQC Software - Top Layers
• Client libraries
- conventional programming l...
©2014 Knowledgent Group Inc. All Rights Reserved
D-Wave Customers
US Government
D-Wave’s QC is a controversial science
sca...
©2014 Knowledgent Group Inc. All Rights Reserved
Hypernumbers and QC
in Perspective
• AQC computing today
- Effective prob...
©2014 Knowledgent Group Inc. All Rights Reserved
Big Data Technology is a Combo of Accelerators
• Reduced code set
- Elimi...
©2014 Knowledgent Group Inc. All Rights Reserved
Big Data IA Foundation
• Information architecture
- Logical data architec...
©2014 Knowledgent Group Inc. All Rights Reserved
Big Data Metadata Ecosystem
©2014 Knowledgent Group Inc. All Rights Reserved
Integrated MDM RDM Architecture
• RDM
- Centrally maintained for the ente...
©2014 Knowledgent Group Inc. All Rights Reserved
Big Data Ecosystem Without EA
•Uncoordinated
•Inconsistent
•Complex
©2014 Knowledgent Group Inc. All Rights Reserved
EA Driven Big Data Ecosystem
©2014 Knowledgent Group Inc. All Rights Reserved
Questions?
• Pragmatic Enterprise Architecture: Strategies to Transform I...
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Enterprise Architecture in the Era of Big Data and Quantum Computing

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Deck from April 2014 Big Data Palooza Meetup sponsored by Knowledgent. Enterprise Architect James Luisi spoke

Summary: Several characteristics identify the presence of big data. Invariably as new use cases emerge, new products emerge to address them. At this point, there are so many use cases, and so many products, that frameworks to organize and manage them are necessary. A couple of examples of useful frameworks to manage and organize include families of use cases and architectural disciplines.

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Enterprise Architecture in the Era of Big Data and Quantum Computing

  1. 1. ©2014 Knowledgent Group Inc. All Rights Reserved Pragmatic Enterprise Architecture In the Era of Big Data and Quantum Computing
  2. 2. ©2014 Knowledgent Group Inc. All Rights Reserved Agenda • Bio / Who is Jim Luisi? • WIIFM / What’s in it for me? • Enterprise Architecture … Past… Future • What is Big Data? • Why has Big Data recently become more compelling? • What architectural disciplines relate to Big Data? • EA View of Big Data • IT Challenge • Big Data TPM… APM… DB Architectures… Quantum Computing • Big Data Use Case Families • Hardware Architecture / QC • Software Architecture / QC • Hypernumbers and QC in Perspective • Big Data Technology Combination of Accelerators • Big Data IA Foundation • Big Data Metadata… MDM… RDM • Big Data Ecosystem • Questions
  3. 3. ©2014 Knowledgent Group Inc. All Rights Reserved Bio • 30 years • Business - Business owner - Big Data in advertising industry - Worked on business side in Wall St • IT - VLDB with government before Big Data - Specialized compartments in NP-complete problem space - Hadoop Big Data architecture with government - Many areas of artificial intelligence are in the Big Data space • Philosophy - holistic view - Enterprise Architecture perspective • Author - Artificial intelligence - Enterprise architecture Who is Jim Luisi?
  4. 4. ©2014 Knowledgent Group Inc. All Rights Reserved • 99.9% of the Big Data mystery will be removed • You’ll be able to engage in the conversation • You can help transform your organization WIIFM What’s in it for me?
  5. 5. ©2014 Knowledgent Group Inc. All Rights Reserved • EA was a corporate committee of generalists • EA focused on a few parts - Capability that answered basic questions crossing – Applications – Tools – Organizational Units EA Past Enterprise Architecture
  6. 6. ©2014 Knowledgent Group Inc. All Rights Reserved • EA will render a holistic view of automation • Standards and governance will manage complexity EA Future Enterprise Architecture
  7. 7. ©2014 Knowledgent Group Inc. All Rights Reserved • Subject matter experts will make the technology ‘D’ • SMEs will collaborate with other SMEs • SMEs will minimize complexity EA Future (cont’d) Enterprise Architecture
  8. 8. ©2014 Knowledgent Group Inc. All Rights Reserved • From a storage perspective - Lots of storage • From a distribution perspective - Data stored in lots of places globally • From a database design perspective - Lots of rows - Lots of tables - Lots of columns • From an algorithmic or mathematical perspective - Lots of variables - Lots of combinations - Lots of permutations - Summed up as one optimal answer among a large number of possibilities What is Big Data?
  9. 9. ©2014 Knowledgent Group Inc. All Rights Reserved Why Has Big Data become more compelling? • Big Data has become - More advanced to address a wider array of business challenges - More friendly to developers and end users - A low cost alternative to traditional transaction databases (OldSQL) - A significant competitive differentiator in many industries • Big Data can now address business use cases involving: - Many enterprise grade requirements - Vast volumes of structured, unstructured, and semi-structured data - High speed and complex data transformation rates - High velocity ingestion rates - Large numbers of concurrent users - Competitive hyper-real-time requirements (e.g., milliseconds, nanoseconds)
  10. 10. ©2014 Knowledgent Group Inc. All Rights Reserved What architectural disciplines relate to Big Data? Business Architecture Business Continuity Marketing Architecture Operations Architecture Disaster Recovery Architecture Storage Architecture Infrastructure Architecture Network SecurityApplication Architecture Reporting Architecture Integration Architecture Application Portfolio Management Technology Portfolio Management Application Security Information Architecture Data Architecture Data Governance Architecture Master Data Management (MDM & RDM) Metadata Management Data Security
  11. 11. ©2014 Knowledgent Group Inc. All Rights Reserved • Variety of Big Data - database technologies - hardware technologies - applications - reporting tools • Variety of sources - Open source providers - Licensed providers - In-house developed - Custom built providers EA View of Big Data Enterprise Architecture
  12. 12. ©2014 Knowledgent Group Inc. All Rights Reserved • All requiring a solid foundation • None of which are free • Adding to the complexity of your IT landscape IT Challenge Enterprise Architecture
  13. 13. ©2014 Knowledgent Group Inc. All Rights Reserved • Competing open source foundations - Apache Software Foundation (ASF) - Free Software Foundation (FSF) - Many individual companies • Competing open source providers of Hadoop - Hortonworks, - Cloudera - MapR - IBM - Microsoft - Intel (IDH) now being sunset Big Data TPM Technology Portfolio Management
  14. 14. ©2014 Knowledgent Group Inc. All Rights Reserved TPM / Competing Big Data Frameworks • and more…
  15. 15. ©2014 Knowledgent Group Inc. All Rights Reserved • Applications use assorted Big Data technologies - Open source - Licensed - Traditional databases (OldSQL) - Traditional Big Data technologies - Non-traditional (NoSQL) - OLTP (New SQL) fully ACID - Proprietary breed - Quantum computing Big Data APM Application Portfolio Management • Approved – Data sources – Use cases – Technologies – Time period • Approved – Data sources – Technologies – Time period • Approved – Data sources – Data target – Time period
  16. 16. ©2014 Knowledgent Group Inc. All Rights Reserved Parent Child Data A B C D E X Hierarchical Parent First Set A Last Set A Data A B1 B2 B3 C X Network Linked List Parent Next Set A Prior Set A First Set C Last Set C Data Data Associative Record A Key Value 'Blue Eyes' 'Height '6'' Pointers Addr2 Addr3 Addr4 Addr4 Addr7 Addr10 Addr10 Addr14 Inverted List Data Addr1 Data Addr2 Data Addr3 Data Addr4 Data Addr5 Data Addr6 Data Data Loan # Date Type Amount PK Data C1 C2 C3 C4 Loan # Payment # Due Date Due Amount Data C1 P1 P2 P3 Loan Payment Coupon PK Relational Header Row3 Row4 cntd Db Page (4K) Footer Row1 Row2 Row3 cntd Row4 Free Space Underlying Storage Structure Just as there are DB Architectures of OldSQL
  17. 17. ©2014 Knowledgent Group Inc. All Rights Reserved There are database architectures of TPM / Traditional Big Data Technologies
  18. 18. ©2014 Knowledgent Group Inc. All Rights Reserved Microsoft HDInsight Intel Distribution (IDH) And there are database architectures of TPM / Non- Traditional Big Data Technologies
  19. 19. ©2014 Knowledgent Group Inc. All Rights Reserved TPM / Newer SQL Types NoSQL is a new way of thinking about databases, founded on the belief that a relational database model may not be the best solution for all situations. NewSQL is a class of modern relational database management systems that seek to provide the same scalable performance of NoSQL systems for online transaction processing workloads while still maintaining the ACID guarantees of a traditional database system. NoSQL NewSQL
  20. 20. ©2014 Knowledgent Group Inc. All Rights Reserved What does ACID mean? • Atomicity - refers to all or nothing for a logical unit of work • Consistency - refers to adherence of data integrity rules that are enforced by the DB • Isolation - refers to the need to enforce a sequence of transactions when updating a database (e.g., two purchasers both trying to purchase the last instance of an item) • Durability - refers to safeguarding information once a commit has been performed to declare successful completion of a transaction.
  21. 21. ©2014 Knowledgent Group Inc. All Rights Reserved TPM / Proprietary Big Data Product Names • US Government - Hypernumbers • Major Financial Conglomerate - Hypercube • High-tech Companies - Named for each client - Hypercompression - LNB - DBX / DB accelerator
  22. 22. ©2014 Knowledgent Group Inc. All Rights Reserved TPM / Quantum Computing • Probabilistic computing - confidence levels - two competing quantum computing architectures • Gate model (aka quantum circuit) - Shor’s algorithm (code breaking algorithm) - present implementations have few qubits - qubit and gate growth is linear - prone to decoherence (quantum physics wave function collapse) - complete error correction theory • Adiabatic quantum computing (AQC) - discrete combinatorial optimization problems (NP-complete) - present implementations are 512 qubits - qubit growth is geometric doubling every 24 months - not prone to decoherence - lacks complete error correction theory
  23. 23. ©2014 Knowledgent Group Inc. All Rights Reserved What are some Big Data Use Case Families? • Document / content management • Online transaction processing (OLTP) • Data warehousing • Real-time analytics • Batch analytics • Geographic information systems (GIS) • Search • Predictive analytics • Deterministic algorithms • Probabilistic
  24. 24. ©2014 Knowledgent Group Inc. All Rights Reserved Document / Content Management Use Cases IT Documents  presentations  word documents  spreadsheets  spreadsheet applications  MS Access applications  standards documents  company policies  architectural frameworks Candidate Requirement Types Maximum document size Maximum ingestion rates Maximum access rates Maximum and mean access speed Maximum number of documents Maximum total storage Maximum concurrent users Supported data types Maximum number of indexes Global accessibility Multi-data center Fault tolerance Developer friendly Business Documents  loan applications and documents  mortgage applications and documents  insurance applications and documents  new account forms Customer Documents  diplomas  customer in  birth certificates  insurance policies  records for tax preparation MongoDB  16 MB limit per document  Developer friendly Cassandra  2 GB limit per document*  Few keys Hadoop HDFS  No practical limit per document size  Slow access Basho Riak  2 GB limit per document  Fault tolerant Couchbase  20 MB limit per document MarkLogic  512 MB limit per document  High speed access Candidate Solutions
  25. 25. ©2014 Knowledgent Group Inc. All Rights Reserved Online Transaction Processing Use Cases Candidate Solutions E-Commerce  Global web-based transactions  Global inventory  Global shipping Marketing  RFID supply chain  Opportunity based marketing  Google glasses applications Consumer Products & Services  In-home medical care  word documents Governmental Capabilities  Military logistics  Homeland security Financial Industry  Global customer exposure  Operational risk Candidate Requirement Types Peak transactions per second Maximum length of transaction System availability System security High volume transaction access paths Number of concurrent connections and sessions Internationalization (e.g., Unicode) Data volume requiring compression Geospatial index support Full text search Index support Sharding support Maximum value size Operating system Minimum memory requirements Real time analytics VoltDB  High speed in-memory  ACID SQLFire  High speed in-memory  ACID NuoDB  ACID Google Spanner  Successor to BigTable  ACID Clustrix  High speed in-memory  Shared-nothing architecture Akiban  ACID
  26. 26. ©2014 Knowledgent Group Inc. All Rights Reserved Data Warehousing Use Cases Candidate Solutions Financial Industry  Underwriting Marketing  Customer analytics  M&A decision making  Divestiture decision making  Campaign management  Customer analytics Science Based Industry  Pharmaceutical development  Genetics research Governmental Capabilities  Materiel management  Intelligence capabilities  Human disease management  Food supply analysis Candidate Requirement Types High data ingestion rates Large data persistence layer Large number of concurrent users Drill downs Internationalization (e.g., Unicode) Data volume requiring compression Comprehensive index support Sharding support Robust SQL interface Backup and restorability Disaster recoverability Commodity hardware Staffing and skill availability Trainability Affordability Vertica  High speed in-memory  High speed DB layer  High ingestion rate  High concurrent users  Real-time analytics Teradata  High speed DB layer  High ingestion rate  High concurrent users  Widest array of connectors Hadoop  High ingestion capacity  Not for real-time analytics Greenplum  High speed in-memory  High speed DB layer  High ingestion rate  High concurrent users  Real-time analytics SAP Hana  High speed in-memory  High speed DB layer  High ingestion rate  High concurrent users  Real-time analytics
  27. 27. ©2014 Knowledgent Group Inc. All Rights Reserved Real Time Analytics Use Cases Candidate Solutions  Investment risk  Operational risk  Financial risk  Market risk  Credit risk Governmental Capabilities  Intelligence capabilities  Human disease management Marketing  Opportunity based marketing  Dynamic web advertising  Regulatory exception reporting  Operational performance  Trading analytics  Algorithmic trading  Real time valuations Financial Industry Candidate Requirement Types High data ingestion rates Large data persistence layer Real-time analytics Large number of concurrent users Drill downs Internationalization (e.g., Unicode) Data volume requiring compression Comprehensive index support Sharding support Robust SQL interface Backup and restorability Disaster recoverability Commodity hardware Staffing and skill availability Trainability Affordability Vertica  High speed in-memory  High speed DB layer  High ingestion rate  High concurrent users  Real-time analytics Greenplum  High speed in-memory  High speed DB layer  High ingestion rate  High concurrent users  Real-time analytics SAP Hana  High speed in-memory  High speed DB layer  High ingestion rate  High concurrent users  Real-time analytics Teradata  High speed DB layer  High ingestion rate  High concurrent users  Widest array of connectors
  28. 28. ©2014 Knowledgent Group Inc. All Rights Reserved Batch Analytics Use Cases Candidate Solutions Candidate Requirement Types High data ingestion rates Large data volumes Validation ability Traceability Integratability Maintainability Affordability Financial Industry  Financial crime  Anti-money laundering  Sanctions  FATCA  Insurance fraud detection  Back testing  Credit risk  Portfolio valuation Hadoop HDFS  MapReduce  Large data volumes Hadoop HBase  MapReduce (one HBase table as input)  Large data volumes Engineering  Equipment failure forecasting Commerce  Collusion forecasting  Legislative forecasting  Regulatory forecasting Marketing  Customer analytics Government al Capabilities  Terrorist activity forecasting  Terrorism event forecasting
  29. 29. ©2014 Knowledgent Group Inc. All Rights Reserved Geographic Information System Use Cases Candidate Solutions Candidate Requirement Types User friendly ACID compliant Flexibility Query able Full spatial function support Address Geocoding  Warrant serving  Emergency services  Crime analysis  Public health analysis Linear Metric Event Modeling  Road maintenance activities  Roadway projects  Traffic & safety analysis Cartography  Hazardous materials tracking  Taxable asset tracking Routing  Evacuation planning  Towing & plowing  Refuse removal  Emergency services Topological  Elevation data  Orthophotography  Hydrography Neo4j  Fully ACID PostGIS  Open source  Geographic support  Built on PostgreSQL Oracle Spatial  Spatial support in Oracle GeoTime  Temporal 3D visual analysis
  30. 30. ©2014 Knowledgent Group Inc. All Rights Reserved Search Use Cases Candidate SolutionsCandidate Requirement Types Usability Flexibilty Maintainability Search as a Service (Saas)  Website search Search Enabled Applications  External data source identification E-discovery  Legal holds  Investigations Data Landscape Mapping  Locating data cross data center Lucidworks  Built on Solr  GUI for common use cases Solr  Flexibility Splunk  Machine generated output
  31. 31. ©2014 Knowledgent Group Inc. All Rights Reserved Predictive Analytics Use Cases Candidate Solutions Financial Industry  Capital markets fraud detection  Wholesale banking fraud detection  Retail banking fraud detection  Insurance fraud detection  Market risk forecasting  Market opportunity forecasting  Operational defect forecasting Engineering  Equipment failure forecasting Commerce  Collusion forecasting  Regulatory shift forecasting Marketing  Customer LTV scoring  Customer defection score Governmental Capabilities  Terrorist activity forecasting  Terrorism event forecasting Candidate Requirement Types High data ingestion rates Large learning sets Supports complex models Rapidly changing learning sets High volume for operational deployment Validation ability Traceability Integratability Maintainability Affordability Fair Isaac HNC  Large feature set  Extensive professional services  High speed  High learning rate  High concurrent users  Real-time deployment SAS CEP & Statistical Packages  High speed  Supports many source types  Comprehensive feature set Ward Systems  Large feature set  Extensive professional services  High speed  High learning rate  High concurrent users  Real-time deployment
  32. 32. ©2014 Knowledgent Group Inc. All Rights Reserved Particular Deterministic Algorithmic Use Cases Candidate Solutions Any Industry  Matrix-vector multiplication  Relational algebra  Computing Selections  Computing Projections  Union, Intersection, Difference  Grouping and aggregation  Reducer size and replication rate  Similarity joins  Graph modeling Candidate Requirement Types High data ingestion rates Large data volumes Validation ability Traceability Integratability Maintainability Affordability Hadoop HDFS  MapReduce  Large data volumes Hadoop HBase  MapReduce  Large data volumes IBM Netezza  Hardware based algorithms
  33. 33. ©2014 Knowledgent Group Inc. All Rights Reserved NP Deterministic Algorithmic Use Cases Candidate Solutions (Proprietary) NP-complete problems - reasonably testable Candidate Requirement Types Massive number of tables involved in joins Massive number of rows involved in joins Large number of calculations High permutation count High combination count Massively distributed Super large numbers Real-time Hyper-numbers Hyper-cubes Hyper-compression DBX LNB NP-hard problems - proof is not reasonably testable
  34. 34. ©2014 Knowledgent Group Inc. All Rights Reserved What is NP? • NP stands for ‘non-deterministic polynomial time’ • Conventional computers can take billions or trillions of years • NP-complete and NP-hard refers only to the ‘check ability’ of a solution in time • NP-complete can be checked in a reasonable length of time • NP-hard cannot be checked in a reasonable length of time
  35. 35. ©2014 Knowledgent Group Inc. All Rights Reserved Probabilistic Use Cases • Cryptography (aka code breaking) • Prime number generation • Traveling salesmen problem • Labeling images and objects within images • NLP meaning extraction • Correlations among Big Data - genetic code correlations • Testing a scientific hypothesis • Machine learning for problem solving - self-programming
  36. 36. ©2014 Knowledgent Group Inc. All Rights Reserved Hardware architecture / AQC • Commercially available platforms - 128 qubit Rainier-4 (D-Wave One) - 512 qubit Vesuvius 3 (D-Wave Two) - 10’ black cube - electromagnetically shielded - digital optical cables - closed liquid helium cooling system (20mK is 100 times colder than interstellar space 2.75K) - cylindrical magnetic shields <1 nanoTesla (nT) • Each qubit has four wave function values - ‘-1-1’, ‘+1+1’, ‘-1+1’, ‘+1-1’ • Each qubit is surrounded by switches - over 180 Josephson junctions per qubit in 3-D space
  37. 37. ©2014 Knowledgent Group Inc. All Rights Reserved Software Architecture / AQC full disjunctive normal form
  38. 38. ©2014 Knowledgent Group Inc. All Rights Reserved AQC Software - Bottom Up • Programming the hardware - is all about managing the switches - to represent a Boolean expression that is to be optimized - to allow the expression to be preloaded with data values - to allow the variables to be set to an unknown state - the slowest step is the annealing process - qubit values are inspected - solution is tested on a conventional computer - calculation is repeated multiple times • In quantum terms you create a mathematical formula to represent the energy state of the system in the form of a Boolean SAT problem • SAT were the first documented example of an NP-complete problem where no know algorithms can solve them in a reasonable length of time using conventional computers*
  39. 39. ©2014 Knowledgent Group Inc. All Rights Reserved AQC Software - Bottom Up (cont’d) • System Application Program Interface - communicates directly with the hardware - requires expertise in quantum mechanics mathematics - is necessary to program only when – developing new functions – exploring quantum physics – conducting QC experiments • Complier - does not convert a higher level language into machine language - requires no knowledge of – QC physics – QC hardware - it is layer that allows the programmer to focus on – bit strings and Boolean mathematics
  40. 40. ©2014 Knowledgent Group Inc. All Rights Reserved AQC Software - Top Layers • Client libraries - conventional programming languages • Frameworks - wrapped functions (aka toolkit) bundled for reuse – supervised binary classification – supervised multiple label assignment – unsupervised feature learning • Applications (top) - this layer interacts with the end user GUI - the GUI itself is on a conventional computer – directly outside the cube – on a conventional network or Intranet or – on the Internet anywhere with access
  41. 41. ©2014 Knowledgent Group Inc. All Rights Reserved D-Wave Customers US Government D-Wave’s QC is a controversial science scale of quantum entanglement
  42. 42. ©2014 Knowledgent Group Inc. All Rights Reserved Hypernumbers and QC in Perspective • AQC computing today - Effective probabilistic approach - Just starting to get traction • Hypernumbers - Highly effective deterministic approach - Hypernumber architecture – Deeper into the polynomial problem space • Promise of QC - Potentially deeper into polynomial timespace
  43. 43. ©2014 Knowledgent Group Inc. All Rights Reserved Big Data Technology is a Combo of Accelerators • Reduced code set - Eliminating large amounts of DBMS code - Eliminating large amounts of OLTP code • Distributed processing - Parallel processing - Loosely or tightly coupled • Compression - Data encoding - Least number of bit data functions • Proprietary hardware - Performing algorithms at the data persistence layer - Massively parallel platforms, networks, etc. - Quantum computer platforms
  44. 44. ©2014 Knowledgent Group Inc. All Rights Reserved Big Data IA Foundation • Information architecture - Logical data architecture - Physical data architecture • Master data management - Shared master data - Reference data management – Code tables – Shared files • Metadata management - Business data glossary - SDLC metadata - Big Data metadata ecosystem
  45. 45. ©2014 Knowledgent Group Inc. All Rights Reserved Big Data Metadata Ecosystem
  46. 46. ©2014 Knowledgent Group Inc. All Rights Reserved Integrated MDM RDM Architecture • RDM - Centrally maintained for the enterprise - Passed to application databases - Managed in HBase for reference data lookups • MDM - Processed prior to Big Data deployment – Landed – Profiled – Cleansed – Standardized – Integrated
  47. 47. ©2014 Knowledgent Group Inc. All Rights Reserved Big Data Ecosystem Without EA •Uncoordinated •Inconsistent •Complex
  48. 48. ©2014 Knowledgent Group Inc. All Rights Reserved EA Driven Big Data Ecosystem
  49. 49. ©2014 Knowledgent Group Inc. All Rights Reserved Questions? • Pragmatic Enterprise Architecture: Strategies to Transform Information Systems in the Era of Big Data and Quantum Computing • James V Luisi / 732-740-2274 • James.Luisi@knowledgent.com • Connect on Linked-in for new books and updates • Available at Amazon.com
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